This relates generally to the process of identifying patterns in data. It may include the detection of lines in image data, computer vision, pattern recognition, and object detection.
In a wide variety of applications, it is desirable to identify patterns within data. In the physical world, the patterns within data may represent image elements in a pictorial image in digital format. Certain features in the image may be extracted for identification of the depicted objects. This may be useful in a wide variety of applications, including medical imaging, detecting objects in images, and generally in image analysis.
One technique for image analysis in computer vision is the Hough Transform. It recognizes global patterns in an image spaced by identifying local patterns in a transformed parameter space. The transformed parameter space is known as a Hough voting table. In the Hough Transform, curves are identified that can be parameters like straight lines, polynomials, circles, ellipses, parabolas, etc. in a suitable parameter space. Therefore, detecting the curves reduces to detecting local maxima in the parameter space to which a large number of pixels are mapped. One advantage of the Hough Transform is the robustness to discontinuous pixels and noise in real world images.